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Bisphosphonate and medication induced necrosis of jaws.

The bisphosphonates are prescribed drugs to prevent bone resorption particularly in patients having certain malignancies. However, there is a potential risk of osteonecrosis of jaws in patients receiving dental treatment. Therefore, it is of interest to evaluate the chances of osteonecrosis in patients referred for management of osteonecrosis in whom dental treatment was carried out by the referring dental professionals. Hence, 86 patients were studied, where 18 patients had developed osteonecrosis particularly after dental extraction of which 13 patients were having underlying malignancy. We

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… Disclaimer: The views and opinions expressed are those of the author(s) and do not reflect the views or opinions of Bioinformation and (or) its publisher Biomedical Informatics. … Biomedical Informatics remains neutral and allows authors to specify their address and affiliation details including territory where required. Bioinformation provides a platform for scholarly communication of data and information to create knowledge in the Biological/Biomedical domain. …

Анализ географических различий между стандартом медицинского обслуживания и пациентами в Канзасе в ходе клинических испытаний.

В настоящем исследовании сопоставляются демографические и географические факторы, связанные с доступом к клеточной терапии химерика антигенного рецептора (КАР)-Т для клеточных заболеваний B-клеток или множественной миеломы: лица, проходящие клинические испытания в Центре раковых заболеваний Университета Канзаса (КСЕК), и лица, получающие стандартную медицинскую помощь (СОС) в системе здравоохранения Университета Канзаса (ТУКХС). МЕТОДЫ: Данные собирались на основе электронных медицинских записей и системы управления клиническими испытаниями КСЕК. Мы провели оценку различий в доступе CAR-T по п

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… D.P.M. provided overall study oversight, informatics infrastructure support, and geospatial methodology expertise. A.R., C.T., O.R., J.M., M.H., A.D.S., and N.A. contributed clinical expertise and assisted with interpretation of the findings. S.P. and D.P.M. drafted the manuscript. … Burus T., Martinez J., DelNero P., et al., “Interinstitutional Approach to Advancing Geospatial Technologies for US Cancer Centers,” JCO Clinical Cancer Informatics 8 (2024): e2400099. 6. Hoffmann M. S., Hunter B. D., Cobb P. W., Varela J. …

Using a digital patient powered research network to identify outcomes of importance to patients with multiple myeloma.

BACKGROUND: Social media platforms give patients a voice by allowing them to discuss their health and connect with others. These unfiltered and genuine reports offer direct access to what matters most to patients. Exploring the patient-reported outcomes discussed in these platforms reveal clinical insights and behavioral patterns of the real-world patient journey. This research study reviewed health-related quality of life (HRQoL) concepts reported by patients with multiple myeloma (MM). METHODS: Data were obtained using the Belong.life patient-powered research network (PPRN) using social med

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… Journal of the American Medical Informatics Association. 2014;21(4):583–586. doi: 10.1136/amiajnl-2014-002758. 9. Humphrey, L., Willgoss, T., Trigg, A. et al. A comparison of three methods to generate a conceptual understanding of a disease based on the patients’ perspective. … Journal of the American Medical Informatics Association. 2018;25(10):1274–1283. doi: 10.1093/jamia/ocy114. 18. Kazandjian D. Multiple myeloma epidemiology and survival: A unique malignancy. Seminars in Oncology. 2016;43(6):676–681. doi: 10.1053/j.seminoncol.2016.11.004. 19. …

Design and development of the Australian and New Zealand (ANZ) myeloma and related diseases registry.

BACKGROUND: Plasma cell dyscrasias (PCD) are a spectrum of disorders resulting from the clonal expansion of plasma cells, ranging from the pre-malignant condition monoclonal gammopathy of undetermined significance (MGUS) to multiple myeloma (MM). MM generates a significant burden of disease on the community and it is predicted that it will increase in both incidence and prevalence owing to an ageing population and longer survival secondary to new therapeutic options. Robust and comprehensive clinical data are currently lacking but are required to define current diagnostic, investigational and

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… A partnership with Monash University was established through the Department of Epidemiology and Preventive Medicine (DEPM), which has an internationally recognised academic epidemiology unit with expertise in registry development, informatics and management of similar clinical registries in ANZ. … This information was then converted into a functional online database by a professional clinical informatics team that is accessed directly by participating sites. …

Proceedings of the CIMAGO Congress - Environment, Genetics and Oncobiology in the 21st Century - Challenges and Opportunities.

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… Portugal; 6 Univ Coimbra, Institute of Integrated Clinical Practice and Laboratory of Evidence-Based and Precision Dentistry, Faculty of Medicine, Coimbra, Portugal; 7 Univ Coimbra, Institute of Endodontics, Faculty of Medicine, Coimbra, Portugal; 8 Univ Coimbra, Laboratory of Biostatistics and Medical Informatics … Ilda Patrícia Ribeiro 1,4,7 1 University of Coimbra, Cytogenetics and Genomics Laboratory, Institute of Cellular and Molecular Biology, Faculty of Medicine, Coimbra, Portugal. 2 Algarve Radiation Oncology Unit – Joaquim Chaves Saúde (JCS), Faro, Portugal. 3 Laboratory of Biostatistics and Medical Informatics …

Mobile Application to Promote Adherence to Oral Chemotherapy and Symptom Management: A Protocol for Design and Development.

BackgroundOral chemotherapy is increasingly used in place of traditional intravenous chemotherapy to treat patients with cancer. While oral chemotherapy includes benefits such as ease of administration, convenience, and minimization of invasive infusions, patients receive less oversight, support, and symptom monitoring from clinicians. Additionally, adherence is a well-documented challenge for patients with cancer prescribed oral chemotherapy regimens. With the ever-growing presence of smartphones and potential for efficacious behavioral intervention technology, we created a mobile health inte

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… L Application of mobile phone technology for managing chemotherapy-associated side-effects Ann Oncol 2007 11 18 11 1887 1892 10.1093/annonc/mdm354 17921245 mdm354 17921245 38 McGee M Gray P A handheld chemotherapy symptom management system: results from a preliminary outpatient field trial Health Informatics … to gamefulness: defining gamification 2011 Proceedings of the 15th International Academic MindTrek Conference: Envisioning Future Media Environments 2011 Tampere 45 Miller AS Cafazzo JA Seto E A game plan: gamification design principles in mHealth applications for chronic disease management Health Informatics …

Реальные данные о риске заражения в нескольких миэломах, обработанных БиТЕ и клетками CAR-T: метаанализ.

ЦЕНТРАЛЬНАЯ РАБОТА: Лечение перенаправления Т-клеток, в том числе использование биконкретных Т-клеток (BITEs) и химерика антигенного рецептора Т-клеток (CAR-T), значительно улучшило результаты лечения повторной или повторной миэломы (RRMMM). Вместе с тем инфекционные осложнения по-прежнему вызывают серьезную озабоченность в плане безопасности, особенно в реальных условиях, когда пациенты являются более разнородными, чем пациенты, проходящие клинические испытания. МЕТОДЫ: Мы провели систематический обзор и метаанализ ретроспективных исследований реального мира, в ходе которых была дана оценка с

DINO-EEG: Montage-Robust Event-Level Seizure Detection in Scalp EEG.

Manual review of long-term clinical electroencephalography (EEG) remains a major bottleneck for large-scale seizure screening in intensive care and epilepsy monitoring settings. Most automated seizure detection methods formulate this task as epoch-level classification, which does not directly provide clinically relevant event boundaries. In addition, many existing systems are sensitive to montage configuration and channel availability, which limits their use in routine clinical practice. We present DINO-EEG, a channel- wise framework for event-level seizure detection in scalp EEG. The method p

A predictive ant colony optimization routing protocol for correlated link stability in wireless body area networks.

BACKGROUND: Wireless Body Area Networks (WBANs) are essential for continuous health monitoring but face critical challenges due to dynamic link instability caused by body movements and strict bio-safety requirements. The fluctuating wireless channel leads to high packet loss, while radio frequency (RF) energy absorption, measured as Specific Absorption Rate (SAR), must remain below 1.6 W/kg to prevent tissue damage. Existing routing protocols often react to failures rather than predict them and rarely co-optimize reliability with thermal safety. METHODS: This paper proposes PACO-CLS (Predicti

Wound3DAssist: A Practical Framework for 3D Wound Assessment.

Managing chronic wounds remains a major healthcare challenge, with clinical assessment often relying on subjective and time-consuming manual documentation methods. Although 2D digital videometry frameworks have aided wound measurement, these approaches struggle with perspective distortion, a limited field of view, and an inability to capture wound depth, especially in anatomically complex or curved regions. To overcome these limitations, we present Wound3DAssist, a practical framework for 3D wound assessment using monocular consumer-grade videos. Our framework generates 3D wound models from sh

AI-powered risk prediction models for preventable maternal mortality in rural settings: a systematic review.

BACKGROUND: Maternal mortality remains disproportionately high in low- and middle-income countries, particularly in rural settings with limited access to skilled obstetric care. Artificial intelligence and machine learning models offer promise for early risk prediction, yet their methodological rigour, applicability, and deployment feasibility in resource-constrained rural contexts remain inadequately synthesised. This systematic review evaluated AI-powered risk prediction models for preventable maternal mortality, with an emphasis on their suitability for rural and low-resource settings. MET

Gut Health Claims and Monetization Features on TikTok: Cross-Sectional Analysis.

BACKGROUND: TikTok has emerged as a major source of health information, yet concerns persist regarding content accuracy and monetization features. Gut health content is particularly vulnerable to misinformation. OBJECTIVE: This study aimed to examine the relationship between creator profession ("medical" vs "nonmedical") and the quality of gut health claims and monetization features on TikTok. METHODS: We conducted a cross-sectional study screening 412 unique TikTok creator accounts identified using the keywords "guthealth," "gutcleansing," and "digestion." The final sample included 174 publ

Ground Reaction Force Estimation From a Single Wrist-Worn IMU Using Biomechanics-Informed Deep Learning.

Long-term gait monitoring in daily environments may support early detection and tracking of sarcopenia-related functional decline. Anterior-posterior propulsive ground reaction force (GRF) reflects push-off function and plantarflexor contribution during walking, but GRF measurement remains largely confined to laboratory force plates. Although wrist-worn devices are widely used, wrist inertial measurement unit (IMU) signals contain both arm-swing kinematics and whole-body dynamics, making GRF estimation from a single distal sensor challenging. This study proposes a Biomechanics-Informed Neural

Anticipating the unexpected: a predictive model for early hospital readmissions in kidney transplant recipients using machine learning.

BACKGROUND: Early hospital readmissions (EHR) within 30 days after kidney transplantation are common and add clinical and financial burden. Early identification of high-risk recipients can guide post-discharge care. We aimed to develop a machine learning model to predict 30-day hospital readmission in adult kidney transplant recipients. METHODS: We conducted a retrospective cohort study of 2,110 adult kidney transplant recipients treated at Colombiana de Trasplantes between July 2008 and December 2023. Patients with allograft thrombosis at the time of transplantation were excluded. Clinical a

Explainable machine learning model for early prediction of ICU death in chronic heart failure with pulmonary infection.

OBJECTIVE: To develop and validate a machine learning model for predicting ICU mortality in CHF patients with pulmonary infection. METHODS: Clinical data were extracted from the MIMIC-IV database, and ICU mortality within 15 days was defined as the primary endpoint. Patients were stratified into HFrEF, HFmrEF, and HFpEF based on left ventricular ejection fraction. Six models were constructed-logistic regression, random forest, light gradient boosting machine, extreme gradient boosting (XGBoost), multilayer perceptron, and k-nearest neighbor-and optimized using cross-validated grid search. Pre

Application of machine learning prediction models for in-hospital mortality in diabetic sepsis patients: a study based on the MIMIC IV database.

BACKGROUND: Sepsis complicated by diabetes mellitus represents a significant clinical challenge in intensive care units (ICUs), associated with substantial morbidity and mortality. Accurate prognostication of in-hospital mortality in this complex patient population is crucial for optimizing clinical decision making, resource allocation, and patient management strategies. METHODS: We conducted a retrospective cohort study utilizing data from the Medical Information Mart for Intensive Care (MIMIC-IV) database. The study focused on adult ICU patients with concurrent diagnoses of sepsis and diabe

AI-powered spectral CT analysis for clinical decision support in carotid vulnerable plaque detection: a deep learning approach.

BACKGROUND: Carotid vulnerable plaques (CVPs) represent a major cause of ischemic stroke, yet current diagnostic methods lack sufficient precision for early detection. Spectral computed tomography (CT) enables detailed plaque characterization, but its clinical utility depends on advanced analytical techniques. METHODS: A Fourier Transform-enhanced U-Net model was developed using spectral CT data from 214 patients at Qiqihar Medical College. Transfer learning was applied for carotid segmentation, supported by extensive data augmentation and ten-fold cross-validation. Model performance was asse

LLM-assisted multidisciplinary decision-making in lung cancer: efficacy of Gemini-2.5-Pro and GPT-5 in a real-world MDT setting.

BACKGROUND: Multidisciplinary team (MDT) review is essential for individualized treatment planning in lung cancer, where decisions often require integration of staging, pathology, molecular findings, performance status, and multimodal treatment options. However, the effective implementation of MDT review remains constrained by limited specialist availability in resource-limited hospitals and by high patient volume, heavy physician workload, and tight clinical schedules in tertiary care centers. Large language models (LLMs) may support MDT workflows by synthesizing clinical information and gene

Informing Policy Responses to Digital Technology Use and Youth Mental Health and Well-Being: Scoping Review.

BACKGROUND: The mental health of children, adolescents, and youths is a growing concern globally, with increasing prevalence of conditions and help-seeking behavior. Alongside traditional factors, digital determinants are now prominent influences on mental health. With digital platforms integral to young people's lives, and problematic social media use rising, policymakers face the challenge of balancing the benefits and risks of young people's digital technology use. Decisive action is hindered by inconsistent evidence on the impacts of social media and other technology use on mental health a

Quality assessment of synthetic data in healthcare: a critical appraisal with a focus on subgroup bias.

BACKGROUND: The creation of synthetic data in healthcare research has emerged as a compelling solution to address challenges related to data privacy, in case of data-sharing and data scarceness for training machine learning models. OBJECTIVE: The current paper aims to provide a critical landscape analysis of synthetic data quality metrics used in healthcare, mapping the state of the field, examining how current fidelity and utility scores often fail to capture weaknesses that appear in specific patient subgroups, and illustrating these blind spots with a lung-cancer case study. We specificall